I was talking with our senior developer recently when our conversation wandered away from whatever we were actually supposed to be discussing and into one of those rabbit holes that seems to be living rent-free in everybody's brain lately: AI.
He had been thinking about his own career and, more specifically, how someone with years of development experience should market those skills now that AI can write code, troubleshoot errors, build applications, explain bugs, and do a suspicious amount of work that used to require a human sitting in front of a computer for hours. It was a fair question, and honestly, probably not the kind of question you want to think about too deeply before you've had enough coffee.
Somewhere in that conversation, something clicked for me. He was basically saying that the tools may change, and yes, parts of the job may get easier or faster, but the years of doing the work still matter because they teach you things that aren't written in the task description. They teach you what to notice, what to question, what usually breaks, when something feels off, and how to respond when there is no neat little tutorial telling you what to do next. And for some reason, that made me open my rΓ©sumΓ©.
The RΓ©sumΓ© That Apparently Requires Cardio
Grabe. I knew I had done a lot of different things over the years, but seeing everything together was mildly ridiculous. Secretary, appointment setter, computer operator, disbursement officer, call center agent, market researcher, transcriptionist, travel expert, writer, social media manager, business owner, barangay councilor, producer, project manager, founder, podcast host... apparently I have been collecting job titles the way other people collect ref magnets.
There was never some grand master plan behind any of this, by the way. Twenty-year-old Maria did not sit down in 2003 and create a beautiful career roadmap that somehow anticipated remote work, social media, automation, artificial intelligence, and whatever else the internet decides to throw at us next. I was mostly doing what made sense at the time, earning money, helping the family, taking opportunities that looked interesting, and occasionally wandering into an entirely new field because my brain went, "Ooh, what's this?" Very scientific career planning.
My first official job was at the Ormoc City Mayor's Office in 2003, where I managed appointments and helped write speeches. Back then, work was simply work. You showed up, figured out what needed to be done, dealt with people, paid attention to details, and learned very quickly which things were actually urgent and which things only felt urgent because somebody was panicking. I had no idea that two decades later, I would be looking back at that job while having conversations about AI and systems.
From there, one thing simply led to another. There were calls to answer, households to research, travel to book, medical reports to transcribe, articles to write, businesses to run, people to serve, projects to manage, content to produce, and a seemingly endless number of things I had to figure out because "I don't know how" has never been particularly effective at stopping me. Looking at the whole timeline now, my career doesn't resemble a ladder. It looks more like somebody gave me access to a giant control panel and I kept pressing every interesting button to see what happened.
The Job Description Was Never the Whole Job
Transcription is probably one of the best examples. I've spent a huge chunk of my working life transcribing, and I've written before about how much I genuinely enjoy typing, which I realize is not everybody's idea of a good time. The clickety-clack of a keyboard makes me happy, editing documents somehow scratches a very particular part of my brain, and catching a weird spelling or grammar mistake can be far more satisfying than it has any right to be.
On paper, transcription sounds simple. You listen, then you type what you hear. Except anyone who has actually done it knows there is a lot happening between those two things, especially when the audio is bad, the speaker mumbles, a medical term appears out of nowhere, a person's name looks wrong, or somebody changes direction halfway through a sentence and assumes everybody listening knows exactly what they're talking about.
After years of doing that, you develop an instinct for checking things. Something sounds off, so you rewind it. A word doesn't make sense in context, so you verify it. A name looks suspicious, so you check the spelling instead of confidently submitting something that looks polished but is completely wrong. That instinct did not come from typing faster. It came from doing the work long enough to know when not to trust the first answer.
Now AI transcription exists, and honestly, I love it. I am not sitting here mourning the days when every single second of audio had to pass manually through my fingers. If technology would like to take repetitive work off my plate, please... be my guest. I have other things to do.
But automatic transcription also made something very obvious to me. The machine can produce the words, but experience taught me when to question them. The task was transcription. The deeper skill was attention.
Customer Service Was Never Just Answering Calls
The same thing happened when I looked back at my call center and customer service years. Technically, you could describe that work as answering calls and helping customers, which sounds very straightforward until you have spoken to somebody who is already annoyed before you've even finished saying hello. Suddenly, "answering calls" becomes listening, interpreting, calming somebody down, solving the real problem, explaining something clearly, and occasionally maintaining your sanity while the other person temporarily misplaces theirs.
You learn pretty quickly that the problem someone describes is not always the actual problem. Sometimes they need an answer. Sometimes they need reassurance. Sometimes they need you to stop explaining and simply listen for thirty seconds because nobody else has. Over time, you learn tone, timing, patience, and the very useful skill of knowing when a technically correct response is still the wrong response for that particular human being.
Today, AI can draft a customer response for me in seconds. It can make the wording polite, concise, professional, apologetic, warm, firm, or whatever else I ask it to do. That's extremely useful. Years of dealing with people taught me whether I should actually send it.
Then Came All the Side Quests
Once I started looking at my rΓ©sumΓ© through that lens, all the random jobs began talking to each other. Writing taught me how to take something complicated and make it understandable. Running businesses taught me that ideas are fun until somebody has to answer the customer, pay the bills, fix the problem, follow up on the order, and figure out why something that worked perfectly yesterday has suddenly decided to stop cooperating today.
Project management taught me to look at moving parts and ask what might break before it actually does. Government work taught me responsibility because somebody else's schedule, request, deadline, or expectation depended on me paying attention. Research taught me to question what I was looking at, while content work taught me that even a really good idea can sit completely unnoticed if nobody knows how to explain its value.
And then there are the career chapters that look even more random from the outside. Running a soap business. Serving as a barangay councilor. Blogging about beauty, travel, parenting, technology, and whatever else had taken over my brain that particular month. Producing podcasts. Learning systems. Building automations. Spending an unreasonable amount of time around mechanical keyboards because apparently one person can care far too much about how a switch sounds when you press it.
None of those things looked connected while I was doing them. But now I can see how one version of me kept handing something to the next one. The writer helped the project manager explain things better. The customer service person helped the business owner deal with people. The transcriptionist made the editor suspicious of tiny mistakes. The business owner understood why clients cared about certain details, while the person who kept wandering into random interests became surprisingly comfortable with learning new tools.
Mao diay. It wasn't really a collection of unrelated jobs. It was a stack.
And Then AI Showed Up
These days, AI is part of my actual work. I use it to brainstorm, research, analyze information, troubleshoot, organize ideas, work through processes, write, and build things I would never have imagined building myself just a few years ago. The amount of repetitive work it can remove from a normal workday still amazes me, especially when I remember how many hours of my life have been donated to copy-pasting information from one place to another.
Sometimes AI does something in thirty seconds that would have taken me an hour. I'm okay with that. Actually, I'm more than okay with that. I do not particularly need the satisfaction of spending four hours doing something manually simply because Past Maria had to suffer through it. Future Maria deserves nice things.
But the more I use AI, the more obvious it becomes that knowing how to use the tool is only one part of the work. I still need to know what I'm trying to accomplish. I need enough context to recognize when an answer doesn't fit the situation, enough judgment to decide which direction is worth pursuing, and enough experience to notice when something important is missing even though the output looks perfectly convincing.
I also need curiosity. I need to know when to ask another question, when to verify something independently, and when to stop because both the AI and I are clearly getting too confident about a subject neither of us should be confidently discussing. Sometimes the smartest thing you can do with a very powerful tool is know when not to trust it yet.
We're Not Showing Up Empty-Handed
I think that's what I found reassuring about that conversation with our developer. So much of the discussion around AI starts with fear that everything we've spent years learning is suddenly losing value. I understand where that fear comes from because it is strange to watch software perform in seconds something that once required training, practice, and an embarrassing number of late nights.
But we are not showing up to this new era as blank slates. We bring the weird jobs, the difficult customers, the mistakes nobody else saw, the projects that failed, the systems that broke, the clients who taught us how differently people communicate, and the years spent doing something manually enough times that we can now recognize when the automated version got it wrong. We bring context, and context takes time.
That's the part that changed when I looked at my rΓ©sumΓ© after that conversation. For years, I saw it as evidence that my career had gone in a million different directions. I had done this, then that, then something completely unrelated, then apparently wandered into another professional side quest because staying in one lane has never been my strongest personality trait.
Now I see the handoffs. I didn't know in 2003 that managing appointments would eventually help the part of my brain that manages projects. I didn't know years of transcription would make me better at checking AI-generated work. I didn't know blogging would help me communicate systems, or that entrepreneurship would make me understand clients differently, or that being perpetually curious about technology would eventually become part of how I earn a living.
I was just doing the work in front of me. That's usually how experience happens, no? We rarely know which ordinary thing we're learning today will become useful ten years from now, because most of the time we're simply trying to earn, help the family, take the next sensible opportunity, or follow whatever rabbit hole has captured our attention.
Then enough years pass, you open an unnecessarily long rΓ©sumΓ© because of a random conversation with your developer, and the whole thing starts looking less like a collection of unrelated jobs and more like one very long apprenticeship. I still don't know exactly what AI will do to my career five or ten years from now, and I don't think anybody can honestly say they do. Some tasks will disappear, others will become easier, new kinds of work will show up, and I will almost certainly end up learning another tool I swore I didn't have time to learn.
But I'm not meeting any of it empty-handed. I'm bringing the secretary from 2003, the call center agent, the researcher, the transcriptionist, the writer, the business owner, the barangay councilor, the producer, the project manager, the systems builder, and every random side quest in between. Apparently, there have been quite a few versions of me preparing for this moment without any of us realizing it.
Which, now that I think about it, is a fairly accurate description of my entire career. π
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